Title
Scalable Topological Data Analysis And Visualization For Evaluating Data-Driven Models In Scientific Applications
Abstract
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.
Year
DOI
Venue
2020
10.1109/TVCG.2019.2934594
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Keywords
DocType
Volume
Model Evaluation, Deep Learning, High-Dimensional Space, Topological Data Analysis, Inertial Confinement Fusion
Journal
26
Issue
ISSN
Citations 
1
1077-2626
0
PageRank 
References 
Authors
0.34
8
16
Name
Order
Citations
PageRank
Shusen Liu111.03
Rushil Anirudh24613.46
Jayaraman J. Thiagarajan324742.17
Sam Ade Jacobs462.17
Brian Van Essen518315.53
David Hysom600.34
Jae-seung Yeom7668.03
David Hysom813111.10
Luc Peterson900.34
Peter B Robinson1000.34
Harsh Bhatia11858.99
B. K. Spears1232.26
Peer-Timo Bremer13144682.47
Di Wang141337143.48
Dan Maljovec15593.37
Valerio Pascucci163241192.33